LLM ReferenceLLM Reference
Microsoft Foundry

Using Embed v4.0 on Microsoft Foundry

Implementation guide · Embed · Cohere

Serverless

Quick Start

  1. 1
    Create an account at Microsoft Foundry and generate an API key.
  2. 2
    Use the Microsoft Foundry SDK or REST API to call cohere-embed-v4-0 — see the documentation for request format.
  3. 3
    You'll be billed $0.12/1M input. See full pricing.

Code Examples

See Microsoft Foundry documentation for integration details.

About Microsoft Foundry

Microsoft Foundry offers a comprehensive platform-as-a-service for enterprise AI operations. It provides multiple deployment options including Serverless APIs (pay-as-you-go), Global Standard (shared managed capacity), Provisioned Throughput Units (reserved capacity), batch processing, and bring-your-own model deployments. The platform features a unified control plane for models, agents, tools, and observability. Its Agent Service enables building and deploying AI agents with built-in tracing, monitoring, and governance. Evaluation and monitoring tools assess model performance, safety, and groundedness. Foundry supports seamless upgrades from Azure OpenAI with non-destructive migration, maintaining existing deployments while unlocking multi-provider model access and advanced platform capabilities.

Microsoft Foundry is a unified Azure platform-as-a-service offering for enterprise AI operations, model builders, and application development. It provides access to over 1,900 models from Microsoft, OpenAI, Anthropic, Mistral, xAI, Meta, DeepSeek, Hugging Face, and more. Foundry unifies agents, models, and tools under a single management grouping with built-in enterprise-readiness capabilities including tracing, monitoring, evaluations, and customizable enterprise setup configurations.

Pricing on Microsoft Foundry

TypePrice (per 1M)
Input tokens$0.12

Capabilities

Multimodal

About Embed v4.0

Latest multimodal embedding model supporting text, images, and mixed content (e.g., PDFs). Embed v4.0 offers variable embedding dimensions (256, 512, 1024, 1536 default) and supports multiple similarity metrics (Cosine, Dot Product, Euclidean Distance). Ideal for semantic search, classification, and clustering across multimodal data.

Model Specs

Released2025-04-01
Context128k
Architecturetransformer

Provider

Microsoft Foundry
Microsoft Foundry

Microsoft

Redmond, Washington, United States